paragraph40.75ex plus1ex minus.2ex-1emPredicting ADE20k parts
To quantitatively support our claim that we learn object parts, we run experiments on ADE20K [1]} street scenes that feature annotations for 111 different part classes on 1983 images.
We pretrain on COCO and report overclustering results given ground-truth parts annotations.
As shown in Table REF , Leopart improves DINO's parts mIoU by \(1.9\%\)  and \(3\%\)  with a clustering granularity of \(K=500\)  and \(K=1000\)  respectively.
This shows that our method increases object part correspondence.
Also, while the supervised ViT outperformed DINO in transfer learning it is not superior when it comes to discovering object parts.
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